Statistically comparing and matching plural sets of digital data

ABSTRACT

The present invention is embodied in a system and method for statistically comparing a first set of digital data to at least a second set of digital data and matching the first set of digital data to appropriately corresponding portions of the second set of digital data. The first or the second set of digital data can be transformed during statistical analysis to enhance statistical analysis of the digital data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation Application of U.S. patentapplication Ser. No. 09/504,022, filed on Feb. 18, 2000 by SZELISKI, etal., and entitled “A SYSTEM AND METHOD FOR PERFORMING SPARSE TRANSFORMEDTEMPLATE MATCHING USING 3D RASTERIZATION”.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates in general to object detection andtracking, and in particular to a system and method for statisticallycomparing and matching plural sets of digital data.

2. Related Art

Applications for automatic digital object detection and tracking, imageregistration, pattern recognition and computer vision analysis arebecoming increasingly important for providing new classes of services tousers based on assessments of the object's presence, position,trajectory, etc. These assessments allow advanced and accurate digitalanalysis (such as pattern recognition, motion analysis, etc.) of theobjects in a scene, for example, objects in a sequence of images of avideo scene. Plural objects define each image and are typically nebulouscollections of pixels, which satisfy some property. These pixels couldbe the result of some pre-processing operation such as filtering,equalization, edge or feature detection, applied to raw input images.Each object can occupy a region or regions within each image and canchange their relative locations throughout subsequent images of thevideo scene. These objects are considered moving objects, which formmotion within a video scene and can be automatically detected andtracked with various techniques, one being template matching.

Template matching is a class of computer algorithms that is used in manydigital computer applications, such as image registration, patternrecognition and computer vision applications. A template matchingalgorithm defines a function (for example, a metric) that estimates thesimilarity between sets of digital data. In this case, one set ofdigital data is commonly referred to as a template and another set ofdigital data is referred to as an image, wherein the template istypically smaller than the image (for instance, the template can be asmall portion of the image). In computer vision applications, thetemplate usually represents an object of the image that is being trackedand detected (located) within the image. The object can be located bycomputing the metric at various locations (u, v) in the image anddetermining where the metric is maximized.

However, many systems that use template matching are not robust orflexible enough for advanced image registration, pattern recognition andcomputer vision applications due to unfavorable tradeoffs offunctionality for performance (for example, restricting themselves totranslations of the template). Therefore, what is needed is a system andmethod for comparing and matching multiple sets of data by transformingone set of data and performing statistical analyses on the multiplessets of data. Whatever the merits of the above mentioned systems andmethods, they do not achieve the benefits of the present invention.

SUMMARY OF THE INVENTION

To overcome the limitations in the prior art described above, and toovercome other limitations that will become apparent upon reading andunderstanding the present specification, the present invention isembodied in a system and method for statistically comparing a first setof digital data to at least a second set of digital data and matchingthe first set of digital data to appropriately corresponding portions ofthe second set of digital data. In one embodiment, the first or thesecond set of digital data is transformed during statistical analysis toenhance statistical analysis of the digital data.

In one embodiment of the present invention, the system includes a hostprocessor executing software that implements an address generator, anacceptance tester and a statistical comparison processor. The hostprocessor controls the entire process and initially renders orrasterizes the sets of data. The address generator generates addresses,which can reflect a transformation, for the first set of data and thesecond set of data to be compared. The addresses are used by filteringfunctions to generate per-pixel values, such as color values. Theacceptance tester receives the per-pixel values for performing variousconventional pixel tests such as, for example, an alpha test, depthbuffer tests, scissor tests, stencil tests, blending, dithering, logicaloperations, etc., and then determines which pixels are to be used tocontribute to the statistical analysis based on the results of one ormore of the acceptance tests. The statistical comparison processorstatistically analyzes the pixels between the first data set and thesecond data set for comparison purposes. The host processor thenexamines the statistical comparisons computed by the statisticalcomparison processor and makes further processing decisions. The processrepeats until a desired result is computed, such as a match or non-matchbetween the data sets.

As is well known to those skilled in the art, conventional graphicsprocessors or graphics rendering devices, including computer graphicscards and the like, are capable of being programmed to perform anynumber of functions other than simply processing pixels for display on acomputer display device. For example, conventional raster graphicsimplementations typically have a number of buffers with a depth of 16,32, or more bits per pixel. In general, each pixel can be considered tobe a data element upon which the graphics hardware operates. This allowsa single graphics language instruction executed by the graphicsprocessor to operate on multiple data elements.

Since the bits associated with each pixel can be allocated to one tofour components, a raster image can be interpreted as a scalar or vectorvalued function defined on a discrete rectangular domain in the x-yplane. For example, the luminance value of a pixel can represent thevalue of the function while the position of the pixel in the imagerepresents the position in the x-y plane. Alternatively, an RGB or RGBAimage can represent a three or four dimensional vector field definedover a subset of the plane. Consequently, highly parallelizedcalculations can be performed on entire functions or vector fieldsdirectly within the graphics hardware of a conventional computergraphics card or the like. Such uses of conventional graphics hardwarefor performing these types of computations have been well known to thoseskilled in the art for a number of years.

Therefore, in an alternate embodiment, the system is implemented in athree-dimensional (3D) graphics rasterizer or rendering device, such as,for example, a conventional computer graphics card or processor, whichhas been modified to include the aforementioned statistical comparisonprocessor. In this embodiment, the system includes a frame buffer (ablock of graphics memory that represents the display screen) and texturememory (a block of graphics memory that can contain portions of thedisplay screen), in addition to the components discussed above (i.e.,the address generator, the acceptance tester, and the statisticalcomparison processor).

In one embodiment, the first set of digital data is stored in the framebuffer while the second set of data is stored in the texture memory.Also, statistical generation is performed by the statistical comparisonprocessor included with the modified rasterizer, with or withoutactually rendering or writing a 3D digital scene comprised of thedigital data to the frame buffer. In this embodiment, rasterization andrendering techniques and advanced statistical generation and comparisonof the present invention are integrated to form a novel video graphicsdevice or hardware video card for computer systems.

Thus, in one embodiment, the system and method described herein comparesand matches a first set of digital data to a second set of digital data.Further, during a raster transformation of the first and second sets ofdigital data, multiple images of the digital data are placed in texturememory as multiple textures. Then, statistics are gathered concerningthe textures, and the raster transformed sets of digital data arecompared and matched against corresponding portions of each other. Inthis context, the system is described as being implemented in a computersystem including a host processor and a modified graphics processorincluding a texture memory for textures, and a graphics processing chiphaving an address generator, an acceptance tester and a statisticscomparison device.

The statistical comparisons and matching processing are provided as partof the rasterization pipeline of the graphics processor, and the dataextracted from the processes are then recorded for normalizedcorrelations or variations, and for subsequent forwarding to the hostprocessor or alternate processing system for further processing orstorage, as desired. Further, as the data is passed through therasterization pipeline of the graphics processor, statistics between thetextures are gathered and processed via the statistical comparisonprocessor.

The present invention as well as a more complete understanding thereofwill be made apparent from a study of the following detailed descriptionof the invention in connection with the accompanying drawings andappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the drawings in which like reference numbers representcorresponding parts throughout:

FIG. 1 is a block diagram illustrating an apparatus for carrying out theinvention.

FIG. 2 is an overview flow diagram of the present invention.

FIG. 3 is a flow diagram of the operation of the present invention.

FIG. 4 is a general block diagram of the present invention.

FIG. 5 is a block diagram illustrating one embodiment of the presentinvention.

FIGS. 6A-6C are graphical images illustrating a working example of thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the invention, reference is made to theaccompanying drawings, which form a part hereof, and in which is shownby way of illustration a specific example in which the invention may bepracticed. It is to be understood that other embodiments may be utilizedand structural changes may be made without departing from the scope ofthe present invention.

I. Exemplary Operating Environment

FIG. 1 and the following discussion are intended to provide a brief,general description of a suitable computing environment in which theinvention may be implemented. Although not required, the invention willbe described in the general context of computer-executable instructions,such as program modules, being executed by a personal computer.Generally, program modules include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular abstract data types. Moreover, those skilled in theart will appreciate that the invention may be practiced with othercomputer system configurations, including hand-held devices,multiprocessor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. The invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located on both local and remotememory storage devices.

With reference to FIG. 1, an exemplary system for implementing theinvention includes a general-purpose computing device in the form of aconventional personal computer 100, including a processing unit 102, asystem memory 104, and a system bus 106 that couples various systemcomponents including the system memory 104 to the processing unit 102.The system bus 106 may be any of several types of bus structuresincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of bus architectures. The system memoryincludes read only memory (ROM) 110 and random access memory (RAM) 112.A basic input/output system 114 (BIOS), containing the basic routinesthat help to transfer information between elements within the personalcomputer 100, such as during start-up, is stored in ROM 110.

The personal computer 100 further includes a hard disk drive 116 forreading from and writing to a hard disk, not shown, a magnetic diskdrive 118 for reading from or writing to a removable magnetic disk 120,and an optical disk drive 122 for reading from or writing to a removableoptical disk 124 such as a CD ROM or other optical media. The hard diskdrive 116, magnetic disk drive 118, and optical disk drive 122 areconnected to the system bus 106 by a hard disk drive interface 126, amagnetic disk drive interface 128, and an optical drive interface 130,respectively. The drives and their associated computer-readable mediaprovide nonvolatile storage of computer readable instructions, datastructures, program modules and other data for the personal computer100. Although the exemplary environment described herein employs a harddisk, a removable magnetic disk 120 and a removable optical disk 124, itshould be appreciated by those skilled in the art that other types ofcomputer readable media which can store data that is accessible by acomputer, such as magnetic cassettes, flash memory cards, digital videodisks, Bernoulli cartridges, random access memories (RAMs), read onlymemories (ROM), and the like, may also be used in the exemplaryoperating environment.

A number of program modules may be stored on the hard disk, magneticdisk 120, optical disk 124, ROM 110 or RAM 112, including an operatingsystem 132, one or more application programs 134, other program modules136, and program data 138. A user may enter commands and informationinto the personal computer 100 through input devices such as a keyboard140 and pointing device 142. Other input devices (not shown) may includea microphone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit102 through a serial port interface 144 that is coupled to the systembus 106, but may be connected by other interfaces, such as a parallelport, game port or a universal serial bus (USB). A monitor 146 or othertype of display device is also connected to the system bus 106 via aninterface, such as a video adapter 148. In addition to the monitor 146,personal computers typically include other peripheral output devices(not shown), such as speakers and printers.

The personal computer 100 may operate in a networked environment usinglogical connections to one or more remote computers, such as a remotecomputer 150. The remote computer 150 may be another personal computer,a server, a router, a network PC, a peer device or other common networknode, and typically includes many or all of the elements described aboverelative to the personal computer 100, although only a memory storagedevice 152 has been illustrated in FIG. 1. The logical connectionsdepicted in FIG. 1 include a local area network (LAN) 154 and a widearea network (WAN) 156. Such networking environments are commonplace inoffices, enterprise-wide computer networks, intranets and Internet.

When used in a LAN networking environment, the personal computer 100 isconnected to the local network 154 through a network interface oradapter 158. When used in a WAN networking environment, the personalcomputer 100 typically includes a modem 160 or other means forestablishing communications over the wide area network 156, such as theInternet. The modem 160, which may be internal or external, is connectedto the system bus 106 via the serial port interface 144. In a networkedenvironment, program modules depicted relative to the personal computer100, or portions thereof, may be stored in the remote memory storagedevice. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers may be used.

II. General Overview

In general, the present invention is embodied in a system and method forstatistically analyzing and comparing a first group of pixels of adefined portion of a digital scene, such as an object or template withinthe digital scene, to a second group of pixels, such as the entiredigital scene or the image. The template is matched to appropriatelycorresponding portions of the image that represent the template. In oneembodiment, during statistical comparison and matching of the templateand the image, either the first or the second group of pixels is rastertransformed. For instance, either the template or the image isincrementally rotated, scaled, or skewed to enhance the statisticalanalyses.

In particular, first, the system receives digital input, such as theimages or the templates. Further, in one embodiment, this data is thenpre-processed, using a variety of techniques such as filtering,equalization, and edge or feature finding. The system then computesstatistics on either transformed images or transformed templates. Next,the resulting computed statistics are analyzed and new requests based onthe resulting statistics are generated with different transformationsand/or different images until a desired result is computed, namely amatch or non-match between the template and the image.

In one embodiment, the present invention is used as an object detectionand tracking system for computer vision, motion analysis and patternrecognition applications, as a video acceleration system for computergraphics video hardware cards, as a video CODEC (coder/decoder), or anyother suitable device that utilizes statistical comparison and matchingbetween sets of digital data, such as images. In addition, in oneembodiment, the present invention is implemented as computer softwarerunning on a computer system, as instruction sets operating within amicroprocessor for a hardware device, such as a computer graphics videocard, as computer firmware operating within a digital device, such as adigital camera, or any other suitable digital processing system.

As is well known to those skilled in the art, conventional graphicsprocessors or graphics rendering devices, including computer graphicscards and the like, are capable of being programmed to perform anynumber of functions other than simply processing pixels for display on acomputer display device. For example, conventional raster graphicsimplementations typically have a number of buffers with a depth of 16,32, or more bits per pixel. In general, each pixel is considered to be adata element upon which the graphics hardware operates. This allows asingle graphics language instruction executed by the graphics processorto operate on multiple data elements.

Since the bits associated with each pixel can be allocated to one tofour components, a raster image can be interpreted as a scalar or vectorvalued function defined on a discrete rectangular domain in the x-yplane. For example, the luminance value of a pixel can represent thevalue of the function while the position of the pixel in the imagerepresents the position in the x-y plane. Alternatively, color (andalpha) values of an RGB (or RGBA) image can represent a three or fourdimensional vector field defined over a subset of the plane.Consequently, highly parallelized calculations can be performed onentire functions or vector fields directly within the graphics hardwareof a conventional computer graphics card or the like. Such uses ofconventional graphics hardware for performing these types ofcomputations have been well known to those skilled in the art for anumber of years.

A conventional graphics processor includes a number of functionalcomponents including, for example, a frame buffer, texture memory, anaddress generator, and an “acceptance tester”. Note that the termacceptance tester is used here to encompass any of the conventionalper-pixel tests which are typically employed by conventional graphicscards, in combination with a determination as to whether particularpixels will contribute to statistical computations.

For example, among the per-pixel tests performed by the acceptancetester in one embodiment is a conventional “alpha test” for checking tosee whether a pixel being drawn has a 0 alpha. With conventionalgraphics cards, a pixel having an alpha value of 0 is simply skippedrather than being drawn. However, in the context of the presentinvention, since there are two different pixels that are being compared,the comparison is skipped if either pixel has a 0 alpha value. Anotherconventional per-pixel test performed by the acceptance tester in oneembodiment is a “depth buffer” test for determining whether the currentpixel being drawn is visible, based on its current z-buffer value. Inthe context of the present invention, this test is used in oneembodiment to only compare a template against the visible portion of a3D scene. Other conventional per-pixel acceptance tests and operationsinclude scissor tests, stencil tests, blending, dithering, logicaloperations, etc., as described in “The OpenGL® Graphics System: ASpecification (Version 1.2.1),” Copyright© 1992-1999 Silicon Graphics,Inc., Apr. 1, 1999, the subject matter of which is incorporated hereinby this reference.

In addition, many such graphics processors also include an alphablending device. Such components are well known to those skilled in theart, and will only be described in general below. However, in oneembodiment conventional graphics processors, such as a conventionalcomputer graphics card or the like is modified to include a statisticscomparison device for computing statistical information directly frominformation existing in the rendering pipeline of the graphicsprocessor.

Thus, in one embodiment, the system and method described herein comparesand matches a first set of digital data to a second set of digital data.Further, during a raster transformation of the first or second sets ofdigital data, multiple images of the digital data are placed in texturememory as multiple textures. Then, statistics are gathered concerningthe textures, and the raster transformed sets of digital data arecompared and matched against corresponding portions of each other. Inthis context, the system is described as being implemented in a computersystem including a host processor and a modified graphics processorincluding a texture memory for textures, and a graphics processing chiphaving an address generator, an acceptance tester (for performingconventional per-pixel testing, as described above) and a statisticscomparison device.”

The statistical comparisons and matching processing are provided as partof the rasterization pipeline of the graphics processor, and the dataextracted from the processes are then recorded for normalizedcorrelations or variations, and for subsequent forwarding to the hostprocessor or alternate processing system for further processing orstorage, as desired. Further, as the data is passed through therasterization pipeline of the graphics processor, statistics between thetextures are gathered and processed via the statistical comparisonprocessor.

FIG. 2 is an overview flow diagram of the system and method of thepresent invention. In general, the present invention matches a first setof digital data to a second set of digital data by statisticallycomparing the sets of data. Namely, first the system receives a firstset of digital data, such as a template, as a first input, and/orreceives a second set of digital data, such as an image, as a secondinput (step 210). Next, the system 200 raster transforms either thefirst or the second set of digital data and computes statistics on thetransformation (step 212). The system then analyzes the resultingstatistics and makes calculated determinations based on the resultingstatistics for generating new and different transformations on the data(step 214) until a desired result is achieved, such as a match ornon-match between the first and the second set of digital data (step216) or between other sets of data.

III. Details of Components and Operation

It has been observed that textured triangle rasterization performed in aconventional graphics processor or the like closely resembles sparsematching of a template with an image. In support of this observation,the following discussion will include a brief overview of conventionalrendering techniques as known to those skilled in the art. As describedherein, these rendering techniques have been adapted for the purpose oftemplate matching. For example, as is well known to those skilled in theart, triangle rasterization performed using conventional graphicsprocessors involves fetching a set of pixels arranged in a regular orderin one or more subsets of graphics memory (the source texture maps),combining or operating on these values, and then drawing these into aframe buffer. (See for example, “The OpenGL® Graphics System: ASpecification (Version 1.2.1),” Copyright© 1992-1999 Silicon Graphics,Inc., Apr. 1, 1999). Similarly, sparse template matching involvesfetching two regular subsets of graphics memory and then comparing thevalues to accumulate some statistics. Therefore, the only differencebetween traditional graphics rasterization (rendering) and sparsetemplate matching is the statistical comparison of pixels and theaccumulation of these statistics, as described below. Therefore, anunderstanding of conventional triangle rasterization will enable thoseskilled in the art to fully understand sparse matching of a templatewith an image as described herein.

FIG. 3 is a flow diagram of the operation of the present invention andFIG. 4 is a general block diagram of the present invention. Referring toFIG. 3 along with FIGS. 4 and 2, first, new sets of data, such as animage and/or a template is acquired (step 310) by the system 400 andinitialized by the host processor 408. In one embodiment, the hostprocessor 408 then stores the new sets of data in the memory devices.For instance, the first set of data, such as the template, is loadedinto a first memory device 412 and the second set of data, such as theimage is loaded into a second memory device 410. Second, models, such astwo-dimensional (2D) or three-dimensional (3D) models, are rendered andstatistics are accumulated (step 312) by the host processor 408 for thetemplate and the image. Rendering and statistical accumulation isaccomplished using a statistics/comparison device 418 in combinationwith an address generator 414 and an acceptance tester 416, as discussedin detail below.

Rendering using conventional graphics processors or the like typicallyinvolves drawing geometric shapes or primitives, such as polygons, intodedicated memory. It should be noted that the present inventionpreferably uses triangles as the drawing primitive, although there areother primitive types that could be used. In general, in a simplifiedexample of rasterization provided for purposes of explanation, a singletriangle is rendered by taking three vertices v₀, v₁, v₂, with thefollowing fields sx, sy, (the screen space coordinates of the trianglein the first memory device) tu, tv, rhw, (the 2D coordinates of eachvertex in the texture, and a perspective term). In particular, theaddress generator 414 of a conventional graphics processor interpolatesthese parameters (v₀, v₁, v₂, etc.) across the triangle; for each pixelin the first memory device subtended by the triangle in screen space(sx, sy), pixel values in second memory device are used by the addressgenerator 414 to compute an interpolated texture value at thecorresponding interpolated texture location.

Note that the operations described in the preceding paragraph areequivalent to a conventional resampling operation being applied to thetexture, which involves filtering the texture at different locations andat different densities. For example, such resampling operations aredescribed in the context of conventional “texture minification” and“texture magnification” operations in Section 3.8.5 and 3.8.6 of theaforementioned “The OpenGL® Graphics System: A Specification (Version1.2.1),” Copyright© 1992-1999 Silicon Graphics, Inc., Apr. 1, 1999.

The present invention builds on these resampling operations by gatheringcomparison statistics between the RGB color values of the correspondingpixels between the two memory devices 410 and 412, using thestatistics/comparison device 418, depending on the results of theacceptance test performed by the acceptance tester 416. For example, asnoted above, if the alpha or z-buffer values for particular pixelsindicate that those pixels are not visible (e.g., a 0 alpha value), thenstatistics will not be gathered by the statistics/comparison device 418for those pixels.

Third, the host processor 408 reads back resulting statistics (step 314)from the statistics/comparison device 418 and adjusts the 2D/3D modelsbased on the resulting statistics (step 316). Steps 312-316 are repeatedas long as the desired iterations or quality for matching are notachieved (step 318). When the desired iterations or quality for matchingare achieved and if additional images or templates need to be analyzed,steps 310-316 are performed (step 320). However, if no additional imagesor templates need to be analyzed after the desired iterations or qualityfor matching are achieved, then the matching or non-matching results arecompleted (step 322).

In general, the address generator 414 generates addresses for thetemplate and the image that are to be compared. These addresses reflecteither the template or image, or a transformation, such as combinationsof rotations, scales and perspective transforms, of the template orimage. The addresses serve as input to filtering functions that readfrom the images to be compared and generate color values (RGBA) and, ifpresent, Z buffer and other per-pixel values. When present, these valuesare used by the acceptance tester 416 to decide whether to allow thepixel to contribute to the statistics. If the pixel is permitted tocontribute, the color values are sent to a statistics/comparison device418 for statistical analyses and comparison processing. For example, asnoted above, if the alpha or z-buffer values of a pixel indicate thatthe pixel would not be visible, then the pixel will not be allowed tocontribute to the statistical comparison.

The statistics/comparison device 418 contains variables that are updatedfor each pixel based on the input color values. For instance, in oneembodiment of the present invention, statistical analyses for comparingand matching the sets of digital data is accomplished by initiallydefining a function or metric within the statistics/comparison device418 that estimates the similarity between the sets of digital data. Inthis case, one set of digital data is the template and the other set ofdigital data is the image. Further, the template can represent an objectof the image that is being tracked and detected (located) within theimage. The object is then located in the image by computing the metricat various locations (u, v) in the image and determining where themetric is maximized.

In the following examples, T represents the template image and Irepresents the input image. In one embodiment, the statistics/comparisondevice 418 uses a cross-correlation coefficient metric for measuring thesimilarity between the image and the template on an absolute scale inthe range [−1, 1], namely:$\frac{{covariance}\left( {I,T} \right)}{\sigma_{I}\sigma_{T}} = \frac{\sum\limits_{x}\quad{\sum\limits_{y}\quad{\left( {{T\left( {x,y} \right)} - \mu_{T}} \right)\left( {{I\left( {{x - u},{y - v}} \right)} - \mu_{I}} \right)}}}{\sqrt{\sum\limits_{x}\quad{\sum\limits_{y}\quad{\left( {{I\left( {{x - u},{y - v}} \right)} - \mu_{I}} \right)^{2}{\sum\limits_{x}\quad{\sum\limits_{y}\quad\left( {{T\left( {x,y} \right)} - \mu_{T}} \right)^{2}}}}}}}$where μ_(I) and σ_(I) designate the mean and standard deviation of theimage and μ_(T) and σ_(T) designate the mean and standard deviation ofthe template.

Additional examples of variables and computations that are tracked bythe statistics/comparison device 418 in alternate embodiments areillustrated below. For example, to compute the statistic, one or more ofthe following sums are calculated between the template (T) andcorresponding pixels in the image (I) in various embodiments:

1) ΣI and ΣT, the sums of the respective pixel values

2) ΣIT, the sum of the product of the pixel values

3) ΣI² and ΣT² the sums of the squares of the respective pixel values

4) Pixel Count, the number of pixels that have been accumulated

It should be noted that computing these sums may dominate the runtime ofthe pattern recognition or other high-level algorithm that is using themetric.

Also, one embodiment uses a summing metric for template matching thatinvolves summing some function of the difference between the image andtemplate pixels, for example:${f\left( {I,T} \right)} = \left\{ \begin{matrix}{\left( {I - T} \right)^{2},{{{I - T}} \leq \delta}} \\{{{I - T}},{otherwise}}\end{matrix} \right.$where δ is some value less than 20 (for 8-bit unsigned integer pixeldata). A more flexible variation involves computing ΣLUT(f(I,T)), thesum of a lookup based on a function of the pixel values. Two examples off(I,T) are f(I,T)=I−T or f(I,T)=|I−T| (the difference and absolutedifference of the pixel values, respectively).

As mentioned above, in one embodiment, a transform is applied to eitherthe input image or the template, in order to find transformed versionsof the template object. Typical transformations include combinations ofrotations, scales and perspective transforms that are relatively closeto identity (to minimize the size of the search space). All of theabove-described variations share the characteristic that pixels from thetemplate are iterated over pixels in the image, and a calculation isperformed between corresponding pixels. The template is typically smallcompared to the image and static over a large number of templatematching search probes.

In further embodiments, several higher-level search strategies are usedto find the best transformation parameters for a given template. Forexample, one such search strategy involves examining all possiblecombinations of parameters, e.g., examining the template at all possiblepositions, orientations, and scales. Further, some computational savingscan be obtained by working in a multi-resolution hierarchy, i.e., tomatch a reduced size template to a reduced size image, and to then trylocal variations (perturbations) to find a better match.

A search strategy used in another embodiment involves taking derivativesof the matching cost with respect to the unknown parameters, and thenusing a generalized gradient descent technique. In this case, inaddition to summing intensities, (threshold) intensity differences, andintensity products or squares (as in regular, normalized, or robustenhanced cross-correlation), also products of horizontal and verticalderivatives are accumulated with themselves and with the per-pixeldifference. If transformations other than translation are beingestimated, the number of required derivatives and products rise quickly.However, it is possible to amortize the computation of derivatives thatare more complicated and their products by dividing the template up intosmaller regions or patches, and only accumulating simpler derivativesand products on a per-pixel basis.

An additional search strategy used in yet another embodiment is to letthe unknown transformation parameters (or at least their updates) becontrolled by the motion of vertices embedded in the template. Forexample, the template can be a wireframe mesh of an object that is beingtracked. The control mesh is then discretized into finer polygons, suchas triangles, and the triangles surrounding each control vertex are usedto estimate that vertex's motion. For reasonably textured templates,convergence will occur to the same estimate as the full gradient descenttechniques, which explicitly model the interaction between verticescontrolling a common patch or triangle.

In addition, the alpha values, α, of the RGBA (red, green, blue, alpha)values in the input colors are used in one embodiment to weight thestatistics, where α is a number between 0 and 1. For example, if α_(T)is the template alpha and α_(I) the image alpha, then a new α is derivedfrom these values by either selecting one, or by performing aconventional weighted blend between them. In one embodiment, theresulting α is then used to weight the pixel's contribution to theabove-described statistics. Among other things, this allows pixels to beimportance-weighted by the application.

V. WORKING EXAMPLE

The following working example is for illustrative purposes only. FIG. 5is a block diagram illustrating one embodiment of the present invention.In general, similar to FIG. 4, the example system 500 of FIG. 5 includesa host processor 508, a first memory device 512, such as a frame buffer,a second memory device 510, such as a texture memory device, and agraphics processor 513 such as a modified computer video or graphicscard that includes an address generator 514, an acceptance tester 516and a statistics/comparison processor 518. In this example, the framebuffer 512 is a block of graphics memory that represents a display for acomputer screen and texture memory 510 is a block of graphics memorythat may contain portions of the display screen. In addition, theexample system 500 of FIG. 5 also includes a statistics enable switch520 and an alpha blending device 522. In this example, the graphicsprocessor 513 resamples either the first or the second set of data to bematched to each other using a conventional perspective transformation.Note that such perspective transforms are well known to those skilled inthe art, and are described in Section 2.10 of “The OpenGL® GraphicsSystem: A Specification (Version 1.2.1),” Copyright© 1992-1999 SiliconGraphics, Inc., Apr. 1, 1999.

In one embodiment, this example system provides a three-dimensional (3D)graphics rasterizer that is modified as described above to form a novelvideo graphics device or hardware video card for computer systems. Inthis embodiment, the first set of digital data is stored in the texturememory while the second set of data is stored in the frame buffer. Also,statistical generation is performed by the statistics/comparison device518 with or without actually rendering or writing a 3D digital scene tothe frame buffer by routing the data to the statistics/comparison devicevia the statistics enable switch 520. Therefore, for implementing thissystem using a computer video graphics hardware device, the additionalcore logic represented by the statistics/comparison device 518 and thestatistics enable switch 520 is used to compute the statistics and toforward the results back to the host processor 508 upon request. FIG. 5shows computations of the statistics between the texture map and theframe buffer image for tracking statistics on the two input pixel valuesinstead of blending between them (via the alpha blending device 522) andwriting the result to the frame buffer 512.

Namely, when the statistics enable switch 520 is enabled, the graphicsprocessor 513 renders the rasterized information (step 220 of FIG. 2)without writing the results to the frame buffer 512. In contrast, whenthe statistics enable switch 520 is disabled, the graphics processor 513actually renders or writes the rasterized information to the framebuffer and display screen. Conventionally, the alpha blending device 522allows use of an additional (such as a fourth) color component that isnot displayed, but that corresponds to the opacity of a surface. Thisprovides control of the amount of color of a pixel in the source surfaceto be blended with a pixel in the destination surface. However, as notedabove, in the context of the present invention, alpha values associatedwith pixels are instead used for weighting computed statistics.Consequently, when the statistics enable switch 520 is enabled, thestatistics/comparison device 518 uses conventional weighting methods forweighting the statistics relative to the alpha values associated withthe pixels.

In one specific embodiment of the example of FIG. 5, the template istreated as a texture and the frame buffer an image and the displayprimitive for rendering purposes is a triangular polygon. In addition,instead of rasterizing the texture into the frame buffer, certainstatistics are recorded for normalized correlation or other statisticscan be recorded for various embodiments. In one example, if the textureis considered a template and the frame buffer an image, the graphicsprocessor 513 is used to resample the template using a perspectivetransformation. Also, the statistics/comparison device 518 of themodified graphics processor 513 is used to record statistics (ΣT, ΣI,ΣIT, ΣT², ΣI² for normalized correlation, or other statistics for avariation) for later forwarding to the host processor.

The example system 500 is extremely flexible because it has the abilityto intersperse rendering/accumulation commands with accumulator readbackcommands. For example, if a deformable triangular patch is beingtracked, the triangle can be rendered using a number of smallertriangles (such as 16), and the accumulator can read back after each ofthe small triangles has been rendered. This allows host processor 508 tocompute necessary derivatives and sums to compute a full affine motionfor the triangle. Similarly, if a larger template, potentiallyconsisting of a number of triangles, is being tracked, each triangle'saccumulated values are read back in order to compute an independentmotion of each control vertex for the template. It has been observedthat the number of readbacks per rendered model are few enough that theyshould not impose a large burden on a hardware graphics port of acomputer system, such as the exemplary computer system depicted in FIG.1.

Note that either the first or the second set of data to be compared witheach other can be rendered at a number of offsets. This allows the hostprocessor 508 to either explicitly find the best position for the firstset of data, such as the template, or accumulate the requiredinformation to analytically compute the best update. The offsets arepreferably simple integer or fractional (such as ½ pixel) perturbationsto the vertices. As such, it is preferable that the system 500 supportsdifferencing of the deformed data (such as the template) and the targetdata (such as the image) at a number of pixel or sub-pixel shifts. Forexample, in one embodiment, the host processor 508 specifies the shiftamount (for instance d=1 or d=½ pixel), to enable accumulationdifferences not only with the target data, but also with versionsshifted by ±d pixels horizontally and vertically (accumulating 9 timesthe statistics). For software implementations, the speed/memory-hardwaretradeoff is good, where the cost of rasterizing a single pixel is stillseveral cycles.

Another advantage of integrating the rasterization and matching stages(via the statistics/comparison device 518) is that the graphics hardwareis then capable of performing the visibility computation for 3Dmodel-based tracking. The 3D model would be rendered once in order tocompute the z-buffer, and then it would be rendered again to compute the(per-triangle) statistics. Note that rendered pixels which fail thez-buffer test (i.e., the depth buffer test) would be discarded from thecomputation, since they are not visible.

FIGS. 6A-6C are graphical images illustrating a working example of thepresent invention. FIG. 6A shows an image 600 from which a feature ofinterest 610 is to be extracted. FIG. 6B shows the feature of interest610 as a template 615 extracted from FIG. 6A. The feature 610 is theportion of the image 600 that is to be tracked or located in subsequentinput images. FIG. 6C shows a subsequent input image 630 that contains arotated and scaled version of the feature 610. The system of the presentinvention detects the feature 610 by transforming either the template615 or the image 630 (in this case, the template 615 is transformed) andgathering statistics between the transformed template 615 and the inputimage 630. A suitable rasterizer in accordance with the presentinvention, as described above, is used to transform the template 615.

Display primitives, in this case triangles, are used to transform thetemplate 615 and locate it in the input image 630. For instance, themapping of the triangles from template 615 to image 630 is shown byarrows 640, 645. Also, although two display primitives encompass theentire template 615 for rasterizing the template 615, additionalprimitives can be used for rasterizing and they do not necessarily haveto encompass the entire template 615. Further, any suitable transformcan be used. For example, powerful transforms, such as perspectivetransforms, can be applied to the template, as well as the affinetransform depicted in FIG. 6C.

The foregoing description of the invention has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the invention to the precise form disclosed. Manymodifications and variations are possible in light of the aboveteaching. For example, any pre-processing transformation, such asfiltering, equalization, or edge or feature finding, could be applied tothe images before they are input to the system and method of the presentinvention. Thus, it is intended that the scope of the invention belimited not by this detailed description, but rather by the claimsappended hereto.

1. A method for comparing and matching a first set of digital data to atleast a second set of digital data, comprising: using a computergraphics card for raster transforming at least one of the first set ofdigital data and the second set of digital data said rastertransformations including combinations of rotations, scales, perspectivetransforms, and translations; depending upon the results of a pixelacceptance test performed by an acceptance tester included in thecomputer graphics card, performing a statistical comparison between atleast part of the first set of digital data and at least part of thesecond set of digital data using a statistical processor included in thecomputer graphics card; wherein the statistical processor uses pixelalpha values to weight any of the first set of digital data and at thesecond set of digital data; and wherein the statistical comparisonincludes statistically comparing and matching the alpha weighted andraster transformed sets of digital data to appropriately correspondingportions of each other using the statistical processor.
 2. The method ofclaim 1, further comprising using the statistical processor foranalyzing the statistical comparisons and using the computer graphicscard for generating new transformations for matching the sets of data.3. The method of claim 1, further comprising using the statisticalprocessor for statistically comparing the raster transformed sets ofdigital data until a match or non-match between the first and secondsets of data is achieved.
 4. The method of claim 1, wherein the rastertransforming comprises raster transforming at least one of the first orthe second set of digital data and computing statistics on thetransformation.
 5. The method of claim 4, wherein statisticallycomparing and matching comprises analyzing the computed statistics ofthe transformation and calculating new and different transformations onthe digital data.
 6. A method for comparing and matching a first set ofdigital data to at least a second set of digital data, comprising:loading at least one of the first and second sets of digital data into afirst memory device; using a 3D graphics rendering device for renderingmodel transformations and accumulating statistics of the loaded digitaldata, said 3D graphics rendering device modified to include astatistical processor, and said model transformations includingcombinations of rotations, scales, perspective transforms, andtranslations; weighting the accumulated statistics using pixel alphavalues associated with any of the first and second sets of digital data;adjusting the model transformations based on the weighted accumulatedstatistics; and statistically comparing and matching the modeltransformations of the loaded set of digital data to appropriatelycorresponding portions of the other set of digital data.
 7. The methodof claim 6, further comprising statistically comparing the sets ofdigital data until a match or non-match between the first and secondsets of data is achieved.
 8. The method of claim 6, wherein adjustingthe model transformations comprises analyzing the statisticalcomparisons and generating new transformations for matching the sets ofdata. 9-20. (canceled)